The author emphasizes that generative AI models can often produce incorrect answers, making them inconsistent and potentially disastrous. To build trust, the results must be human-interpretable, transparent, and involve human oversight. The author also stresses the importance of permissions, stating that enterprise LLMs must adhere to a company’s privacy rules and data governance policies. The article concludes by stating that while generative AI will be a critical force multiplier for knowledge workers, it is essential to ensure that the models are based on a stable, secure, and trusted foundation.
Key takeaways:
- Generative AI is being adopted rapidly by consumers and businesses, with potential applications in various fields like marketing, technology, and consulting.
- Despite its potential, the rapid adoption of generative AI is risky as it may lead to privacy and security violations. It needs to meet three major requirements: accuracy, trust, and permissions.
- Accuracy is crucial as generative AI models can produce wrong answers confidently, making it difficult for non-experts to identify the flaws. The AI needs to understand an organization's intricacies, policies, language, and collaboration methods.
- Trust and permissions are also critical. The results must be human-interpretable, and the provenance of any generated answer should be clear. Also, the AI must adhere to a company’s privacy rules and data governance policies.